Julian – React, Python, AWS

A global enterprise platform supporting supply chain operations, data analytics, and business automation across multiple departments. The solution provides scalable data processing, full-stack internal applications, and AI-powered automation to streamline ETL workflows, enable real-time analytics, and improve operational efficiency through intelligent engineering workflows.

• Architected scalable data and AI pipelines using Apache Airflow, AWS Glue, and Amazon Athena;
• Developed full-stack web applications using Python (Django/DRF) and React;
• Optimized PostgreSQL database performance and implemented CI/CD pipelines;
• Designed n8n- and Airflow-based automation workflows for data synchronization and LLM-driven notifications;
• Established AI-augmented engineering standards using LLM-powered code generation and AI-assisted workflows;
• Translated scientific and analytical requirements into production-grade services within Agile environments.

A workforce management and compliance platform for the construction and field service industries. The solution helps organizations manage field operations, workforce scheduling, compliance, and operational data through scalable backend services, cloud-based analytics, and AI-powered monitoring to improve system reliability and business efficiency.

• Engineered distributed backend services using Python, Django, and Node.js;
• Managed AWS cloud infrastructure, including Lambda, S3, and EC2, for a high-scale production platform;
• Built and optimized data analytics pipelines using Databricks, AWS Glue, and Amazon Athena;
• Implemented AI-powered monitoring alerts using LLM-based log analysis and anomaly detection;
• Leveraged n8n for workflow orchestration and automated AI-assisted incident response;
• Maintained frontend clients using React, TypeScript, and Angular;
• Contributed to CI/CD automation to ensure seamless full-stack delivery.

An IoT data monitoring platform that enables enterprises to visualize real-time sensor data, manage cloud storage integrations, and monitor operational performance. The solution improves visibility into connected devices while enhancing platform reliability through modern engineering practices and AI-assisted development workflows.

• Built and maintained an IoT data dashboard using Python and React.js;
• Integrated real-time sensor feeds with cloud storage;
• Implemented CI/CD pipelines and automated unit and integration tests;
• Improved system observability and overall reliability;
• Modernized the platform by migrating legacy codebases and upgrading dependencies;
• Integrated the Claude API for AI-generated documentation and automated code refactoring.

An institutional digital asset platform supporting cryptocurrency trading, data processing, and compliance operations. The solution combines scalable backend services, high-throughput analytics pipelines, and secure cloud infrastructure with AI-driven risk assessment to improve operational efficiency, fraud detection, and regulatory workflows.

• Developed backend systems and trading infrastructure for institutional cryptocurrency products using Python, FastAPI, and PostgreSQL;
• Orchestrated high-throughput data processing and analytics pipelines with Apache Airflow and Databricks;
• Transformed raw transactional data into risk and compliance datasets using pandas and NumPy;
• Deployed and scaled microservices on AWS using Docker and Kubernetes;
• Implemented secure architectures, automated testing, and GitOps CI/CD practices;
• Prototyped AI-driven risk assessment modules using LLM embeddings and RAG patterns for fraud detection;
• Utilized AI code assistants to accelerate backend service development and standardize architectural documentation.

A digital platform for a plant-based nutrition and media brand that delivers educational content, recipes, and wellness resources to a broad user base. The solution enhances user engagement through scalable web applications, optimized background processing, and AI-powered content personalization and automation.

• Developed frontend features using Vue.js and backend services with Django;
• Optimized RESTful API performance for web application services;
• Optimized background job processing using Celery and Celery Beat;
• Containerized development environments using Docker and Docker Compose;
• Implemented CI/CD pipelines with CircleCI;
• Maintained unit tests and contributed to mobile deployment workflows using Bitrise;
• Integrated n8n workflows for content personalization and AI-driven user engagement automation.

A health technology company focused on building data-driven products and services. Through UruIT Service, this project involved developing backend services, managing PostgreSQL databases, automating data workflows, and exploring AI-enhanced data querying capabilities using RAG patterns and vector embeddings.

• worked on multiple backend services using Python and Django;
• managed PostgreSQL databases and built frontend components with React and Redux;
• automated data workflows using Python scripts and Celery;
• performed analytics with DBT and Mode;
• optimized query performance for database operations;
• handled deployments on Heroku;
• improved system observability with structured logging;
• completed HIPAA compliance training;
• explored RAG patterns with vector embeddings for AI-enhanced data querying and reporting.

This project focused on building a job post management platform, providing technical leadership, full-stack development, architectural guidance, and secure cloud infrastructure using AWS services.

• provided technical leadership and participated in front-end and back-end development for a job post management platform;
• collaborated with a team of five developers on feature delivery and technical decisions;
• conducted systematic code reviews and provided architectural guidance;
• collaborated on CI/CD implementation and quality standards;
• developed backend services using Django REST Framework;
• built frontend features using Vue.js;
• ensured scalable API design;
• orchestrated cloud infrastructure using AWS EBS, EC2, RDS, and S3;
• implemented secure deployment practices.

This project focused on building a RESTful API system to manage the university’s academic production, using a modern backend architecture with Ruby on Rails, containerized environments with Docker, and a frontend built with Angular.

• developed a RESTful API system for managing the university’s academic production;
• built backend services using Ruby on Rails;
• managed data persistence using SQLite;
• containerized the application using Docker;
• developed frontend features using Angular;
• supported the maintenance and evolution of the academic production management platform.